Hypothesis: Media tends to bias reporting towards reader preferences

Data:

Methodology: To measure the political slant of german online newspaper, the topics addressed in newspapers are compared with topics addressed in press releases of political parties…

  1. Estimate the slant index:
  1. Sentiment analysis: Estimate the sentiment of a document, using a dictionary-based (“bag of words”) approach
  2. Topic probabilty: Find the latent topics in the corpus using a structural topic model
  3. Weighted topic probabilty: Multiply the sentiment value of each document with its topic probability vector
  4. Correlation: Estimate the correlation of the weighted topic probabilty between media and press releases
  1. Compare slant index with reader preferences:
  1. Reutes digital insititue
  2. …
set.seed(4556)

library(stm)
library(tidyverse)
library(dplyr)
library(ggthemes)
library(tidytext)
library(patchwork)

rm(list = ls())

color1 <- "#778899"
color2 <- "#808080"
color3 <- "#000000"

# load press releases
load("../output/pressReleases.Rda")
# load news article data
load("../output/data_step2.Rda")

# combine both
model_df <- btw %>% 
  dplyr::mutate(date = as.Date(date),
         type = "news",
         source = medium) %>%
           bind_rows(.,pressReleases %>% 
                       mutate(source = party)) %>%
  dplyr::mutate(doc_index = as.numeric(rownames(.)))

Inspect data

model_df %>%
  group_by(type, source) %>%
  tally() %>%
  ggplot(aes(reorder(source, n),n, fill = type)) +
  geom_col(show.legend = F) +
  theme_hc() +
  coord_flip() +
  labs(y="# documents", x=NULL, title = "Number of documents", subtitle = "June 2017 - March 2018") +
  facet_wrap(~type, scales = "free")

Wordclouds

Before pre-processing:

Pre-processed Data:

TF-IDF

The statistic tf-idf (term frequency - inverse document frequency) is intended to measure how important a word is to a document in a collection (or corpus) of documents.

The inverse document frequency for any given term is defined as

\[ idf\text{(term)}=\frac{n_{\text{documents}}}{n_{\text{documents containing term}}} \]

Bigrams

Sentiment analysis

To measure the tone (or sentiment) of an article a dictionary-based method is applied. To conduct such an analysis, a list of words (dictionary) associated with a given emotion, such as negativity is pre-defined. The document is then deconstructed into individual words and each word is assigned a sentiment value according to the dictionary, where the sum of all values results in the emotional score for the given document. Such lexical or “bag-of-words” approaches are widely presented in the finance literature to determine the effect of central banks’ monetary policy communications on asset prices and real variables.

The present paper uses a dictionary that lists words associated with positive and negative polarity weighted within the interval of \([-1; 1]\). SentimentWortschatz, is a publicly available German-language resource for sentiment analysis, opinion mining, etc.. The current version of SentiWS (v1.8b) contains 1,650 positive and 1,818 negative words, which sum up to 15,649 positive and 15,632 negative words including their inflections, respectively.

The sentiment score for each document \(d\) is calculated based on the weighted polarity values for a word, defined on an interval between -1 and 1. The score is then calculated from the sum of the words in a document (which can be assigned to a word from the dictionary) divided by the total number of words in that document:

\[ \text{SentScore}_d = \frac{|\text{positive polarity score}_d| - |\text{negative polarity score}_d|}{|\text{TotalWords}_d|} \]

sent <- c(
  # positive Wörter
  readLines("dict/SentiWS_v1.8c_Negative.txt",
            encoding = "UTF-8"),
  # negative W??rter
  readLines("dict/SentiWS_v1.8c_Positive.txt",
            encoding = "UTF-8")
  ) %>%
  
  lapply(function(x) {
  # Extrahieren der einzelnen Spalten
  res <- strsplit(x, "\t", fixed = TRUE)[[1]]
  return(data.frame(words = res[1], value = res[2],
                    stringsAsFactors = FALSE))
  }) %>% 
  
  bind_rows %>%
  mutate(word = gsub("\\|.*", "", words) %>% 
           tolower, value = as.numeric(value),
         type = gsub(".*\\|", "", words)) %>%
  
  # nur adjektive oder adverben
  # filter(type == "ADJX" | type == "ADV") %>%
  # manche Wörter kommen doppelt vor, hier nehmen wir den mittleren Wert
  group_by(word) %>%
  dplyr::summarise(polarity = mean(value)) %>% ungroup %>%
  # Delete "Heil" (wegen Hubertus Heil)
  filter(!grepl('heil',word,ignore.case = T)) %>%
  # welcome to hell (g20)
  filter(!grepl('hell',word,ignore.case = T)) %>%
  # filter values that that score between -0.1 and +0.1 
  filter(!dplyr::between(polarity, -0.1,0.1))
sent_token <- model_df %>% 
  select(doc_index, date, source, title_text) %>%
  unnest_tokens(word, title_text) %>% left_join(., sent, by = "word")
sent_df <- sent_token %>% 
  group_by(doc_index) %>% 
  dplyr::summarise(n = n(),
                   polarity_sum = sum(polarity, na.rm = T),
                   sentiment = polarity_sum/n)

save(sent_df, file="../output/sentiment.Rda")
model_df <- left_join(model_df, sent_df, by="doc_index")

plot <- model_df %>%
  mutate(sentiment = sentiment*1000) %>%
  group_by(type) %>%
  mutate(avg_sent_type = median(sentiment, na.rm = T)) %>%
  ungroup() %>%
  group_by(source, type) %>%
  dplyr::summarise(avg_sent_type = mean(avg_sent_type, na.rm = T),
                   sent_mean = median(sentiment, na.rm = T)) %>% ungroup()
  

ggplot(plot, aes(source, sent_mean, 
                 label = round(sent_mean,2),
                 color = type)) +
  geom_point(show.legend = F, alpha = 0.8, size=3) +
  geom_hline(aes(yintercept =avg_sent_type), linetype=2, color = "darkred") +
  geom_hline(aes(yintercept =0), color = color1) +
  geom_text(show.legend = F, color="black", size=2, vjust=-1) +
  coord_flip() +
  facet_grid(type~., scales = "free_y") +
  labs(x=NULL, y=NULL, title = "Sentiment score") +
  theme_hc()

#ggsave("../figs/sentiment.png", height = 4, width = 6, dpi=300)

Structural Topic Model

Parties want the media agenda to be congruent with their own agenda to define the issue-based criteria on which they will be evaluated by voters ( Eberl, 2017 ). Thus, parties instrumentalize their press releases in order to highlight issues that they are perceived to be competent on, that they “own” and that are important to their voters ( Kepplinger & Maurer, 2004 ). Editors can select from this universe and decide which of these topics will be discussed in the news. In that sense the ideological content of a newspaper refers to the extent to which the topics promoted by the parties correlate with the topics discussed in the news articles.

To discover the latent topics in the corpus of press releases and news articles, a structural topic modeling (STM) developed by Roberts (2016) is applied. The STM is an unsupervised machine learning approach that models topics as multinomial distributions of words and documents as multinomial distributions of topics, allowing to incorporate external variables that effect both, topical content and topical prevalence.

Build Corpus

Select Model

STM assumes a fixed user-specified number of topics. There is not a “right” answer to the number of topics that are appropriate for a given corpus (Grimmer and Stewart 2013). Roberts et al. (2016) propose to measure topic quality through a combination of semantic coherence and exclusivity of words to topics. Semantic coherence is a criterion developed by Mimno et al. (2011) and is closely related to pointwise mutual information (Newman et al. 2010): it is maximized when the most probable words in a given topic frequently co-occur together.

Using the function searchK several automated tests are performed to help choose the number of topics including the average exclusivity and semantic coherence as well as the held out likelihood (Wallach et al. 2009) and the residuals (Taddy 2012).

Run Model

This process revealed that a model with 50 topics best reflects the structure in the corpus. Furthermore, the source of a document (each party as well as each media outlet represent one source) is used as covariate in the topic prevalence. In other words, I assume that the probability distribution of topics for a specific document is influenced by the source of that document. Additionally the type of that source (news website or party) is used as a covariate for the term frequency as I assume that the words used for the same topic differ between news articles or press releases.

STM Results

library(stm)
library(tidyverse)
library(tidytext)
library(ggthemes)
library(xtable)

rm(list = ls())
color <- "#b7b7b7"
color1 <- "#778899"
color2 <- "#808080"
color3 <- "#000000"
source("func/functions.R")

# stm results
load("../output/models/finalmodel_50.RDa")
# sentiment results
load("../output/sentiment.Rda")
k <- stmOut$settings$dim$K
  
model_df <- model_df %>%
  dplyr::mutate(doc_index = as.numeric(rownames(.))) %>%
  left_join(., sent_df, by="doc_index")

stmOut$settings$call
## stm(documents = out$documents, vocab = out$vocab, K = k, prevalence = ~source, 
##     content = ~type, data = out$meta, init.type = "Spectral", 
##     max.em.its = 75)
length(stmOut$vocab)
## [1] 74343

Label topics

To explore the words associated with each topic we use the words with the highest probability in each topic. As we included the source type (press release or news paper) as a control for the topical content (the word distribution of each topic), we have two different labels for each topic.

sagelabs <- sageLabels(stmOut, 20)
newsLabels <- as.data.frame(sagelabs$cov.betas[[1]]$problabels) %>%  
  transmute(topic = as.numeric(rownames(.)),
            topic_name_news = paste(V1,V2,V3))

pressLabels <- as.data.frame(sagelabs$cov.betas[[2]]$problabels) %>% 
  transmute(topic = as.numeric(rownames(.)),
            topic_name_press = paste(V1,V2,V3))

topics.df <- left_join(newsLabels, pressLabels, by="topic") %>%
  mutate(label1 = paste(topic_name_news, topic_name_press))

for (i in seq(k)) {
  label <- paste(unique(unlist(strsplit(topics.df$label1[i], " "))), collapse = " ")
  topics.df$joint_label[i] <- paste("Topic",topics.df$topic[i],":", label)
}

topics.df %>% 
  select(joint_label, topic_name_news, topic_name_press) %>% 
  htmlTable::htmlTable(align="l", header = c("Joint label", "News articles","Press releases"),
                       rnames = F)
Joint label News articles Press releases
Topic 1 : schulz spd martin sichert schulz spd martin schulz martin sichert
Topic 2 : wahlkampf duell tv kanzlerin merkel wähler wahlkampf duell tv kanzlerin merkel wähler
Topic 3 : grünen cdu niedersachsen schwarz jamaika grünen cdu niedersachsen grünen schwarz jamaika
Topic 4 : spd union groko koalit koalitionsvertrag großen spd union groko koalit koalitionsvertrag großen
Topic 5 : the of to start up the of to start up the
Topic 6 : diesel auto fahrverbot bundesregierung diesel auto fahrverbot diesel bundesregierung auto
Topic 7 : twitter facebook netz medien meinungsfreiheit twitter facebook netz medien facebook meinungsfreiheit
Topic 8 : seehof csu söder sozial bayern gerechtigkeit seehof csu söder sozial bayern gerechtigkeit
Topic 9 : merkel angela kanzlerin bundeskanzlerin merkel angela kanzlerin merkel angela bundeskanzlerin
Topic 10 : trump russland schröder sanktionen nato trump russland schröder russland sanktionen nato
Topic 11 : koalit spd neuwahlen digitalisierung deutschland bildung koalit spd neuwahlen digitalisierung deutschland bildung
Topic 12 : afghanistan abschiebung abschiebungen bundesregierung jelpk afghanistan abschiebung abschiebungen afghanistan bundesregierung jelpk
Topic 13 : stadt flüchtling innenminist flüchtlingen grenzen stadt flüchtling innenminist flüchtling flüchtlingen grenzen
Topic 14 : prozent spd afd theurer jahr prozent spd afd prozent theurer jahr
Topic 15 : israel antisemitismu juden dr europarat demokrati israel antisemitismu juden dr europarat demokrati
Topic 16 : nordrhein westfalen nrw regionen osten ländlichen nordrhein westfalen nrw regionen osten ländlichen
Topic 17 : euro milliarden geld rent euro milliarden geld euro milliarden rent
Topic 18 : spd verhandlungen union zentral wichtig spd verhandlungen union zentral wichtig verhandlungen
Topic 19 : afd gauland weidel alic afd gauland weidel weidel alic gauland
Topic 20 : berlin polizei amri air bundesregierung berlin polizei amri berlin air bundesregierung
Topic 21 : maa steinmeier gesetz bundestag gesetzentwurf maa steinmeier gesetz gesetz bundestag gesetzentwurf
Topic 22 : kohl helmut kanzler europa macron deutsch kohl helmut kanzler europa macron deutsch
Topic 23 : fdp grünen jamaika frage lindner fdp grünen jamaika frage lindner grünen
Topic 24 : bundeswehr soldaten leyen bundesregierung nato bundeswehr soldaten leyen bundeswehr bundesregierung nato
Topic 25 : frauen kinder männer gewalt opfer frauen kinder männer frauen gewalt opfer
Topic 26 : csu cdu union sude obergrenz seehof csu cdu union sude obergrenz seehof
Topic 27 : hamburg gipfel polizei trump usa hamburg gipfel polizei trump usa gipfel
Topic 28 : afd petri partei poggenburg sachsen andré afd petri partei poggenburg sachsen andré
Topic 29 : verfassungsschutz deutschland bka erdogan bundesregierung verfassungsschutz deutschland bka erdogan deutschland bundesregierung
Topic 30 : daten informationen unternehmen banken de ezb daten informationen unternehmen banken de ezb
Topic 31 : raf berlin schleyer verbrauch behörden daten raf berlin schleyer verbrauch behörden daten
Topic 32 : flüchtling jahr zahl kommunen mietpreisbrems wohnungsbau flüchtling jahr zahl kommunen mietpreisbrems wohnungsbau
Topic 33 : welt menschen politik menschenrecht deutschland welt menschen politik menschen menschenrecht deutschland
Topic 34 : bundestag afd fraktion antrag deutschen bundestag afd fraktion bundestag antrag deutschen
Topic 35 : muslim islam kirch kind herdt deutschland muslim islam kirch kind herdt deutschland
Topic 36 : prozess gericht bundesanwaltschaft türkei menschenrecht türkischen prozess gericht bundesanwaltschaft türkei menschenrecht türkischen
Topic 37 : link linken partei gabriel außenminist politik link linken partei gabriel außenminist politik
Topic 38 : zdf sendung talk öffentlich rechtlichen rundfunk zdf sendung talk öffentlich rechtlichen rundfunk
Topic 39 : türkei erdogan deutschland eu lambsdorff europäischen türkei erdogan deutschland eu lambsdorff europäischen
Topic 40 : höcke afd npd georg pazderski brandner höcke afd npd georg pazderski brandner
Topic 41 : cdu spahn politik tauber heiner cdu spahn politik tauber heiner politik
Topic 42 : deutschland europa macron klimaschutz landwirtschaft deutschland europa macron deutschland klimaschutz landwirtschaft
Topic 43 : eu deutschland europa eu deutschland europa eu deutschland europa
Topic 44 : familiennachzug flüchtling deutschland migranten familiennachzug flüchtling deutschland deutschland familiennachzug migranten
Topic 45 : cdu spd twesten de bundestag maizièr cdu spd twesten de bundestag maizièr
Topic 46 : berlin jahr tag opfer freiheit berlin jahr tag opfer tag freiheit
Topic 47 : august cdu spd wahl hondura wähler august cdu spd wahl hondura wähler
Topic 48 : ge ten be nen re ge ten be ge nen re
Topic 49 : kinder deutschland studi bildung kinder deutschland studi kinder bildung deutschland
Topic 50 : terror köln ring endlich muslim terrorismu terror köln ring endlich muslim terrorismu

Topic probabilty

theta <- as.data.frame(stmOut$theta) %>% # get all theta values for each document
  
  mutate(doc_index = as.numeric(rownames(.))) %>%
  # convert to long format
  gather(topic, theta, -doc_index) %>%
  mutate(topic = as.numeric(gsub("V","",topic))) %>%
  
  # join with topic df
  left_join(., topics.df, by="topic") %>%
  
  # join with model_df
  left_join(., model_df %>% 
              select(date,type,source,doc_index,title_text,sentiment), by="doc_index") %>%  
  
  # delete documents that are published in Mai 2017
  mutate(
    year = lubridate::year(date),
    month = lubridate::month(date)
    ) %>%
  filter(month != 5)

Each document has a probabilty distribution over all topics, e.g.

# select a random document
doc <- sample(unique(theta$doc_index),1)

sample <- theta %>% filter(doc_index == doc) 
caption <- model_df %>% filter(doc_index == doc) %>% select(title, source)

sample %>%
  ggplot(aes(reorder(joint_label,desc(topic)), theta)) +
  geom_col(fill = color1) +
  coord_flip() +
  ylim(c(0,1)) +
  theme_hc() +
  labs(x = NULL, y = NULL, caption = paste("title:",caption$title,"(",caption$source,")"))

#ggsave("../figs/doc_topic_distr.png", height = 8, width = 8)

What is the document about?

sample$title_text[1]
## [1] "Bundestagsbericht - Geheimdienste überwachten 3747 Telefonanschlüsse Die deutschen Geheimdienste haben im vergangenen Jahr deutlich mehr Arbeit geleistet als 2015 - jedenfalls was Überwachung im Bereich der Telekommunikation angeht.\n\n► Insgesamt wurden 3747 Telekommunikations-Anschlüsse überwacht, das sind 32 Prozent mehr als im Vorjahr (2838)!\n\nLaut Bericht des Parlamentarischen Kontrollgremiums des Bundestages (PKG) waren von den Abhörmaßnahmen 261 Personen betroffen. 2015 waren es 193 (plus 35 Prozent). Die meisten Maßnahmen wurden vom Bundesamt für Verfassungsschutz angeordnet.\n\nIm ersten Halbjahr wurden 432 Hinweise des Verfassungsschutzes aus Abhörmaßnahmen an 33 ausländische Dienste weitergegeben. Im zweiten Halbjahr gingen 542 Meldungen an 39 ausländische Dienste.\n\n* Der Bundesnachrichtendienst durchforstete Telekommunikations-Verkehr (z. B. E-Mails) mithilfe von 2307 Suchbegriffen auf \"internationalem Terrorismus\". 34 Fälle waren \"nachrichtendienstlich relevant\".\n\n* Im Bereich illegaler Rüstungsexporte wurden 379 Suchbegriffe verwendet und 19 Hinweise entdeckt."

The figure below displays the topics ordered by their expected frequency across the corpus.

topicmean <- theta %>%
  group_by(topic, joint_label) %>%
  summarise(frequency = mean(theta)) %>%
  ungroup() %>%
  arrange(desc(frequency)) %>%
  mutate(order = row_number())
topicmean %>%
  ggplot(aes(reorder(joint_label, -order),frequency, fill=frequency)) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
  theme_hc() +
  labs(x=NULL, y=NULL) 

#ggsave("../figs/topic_proportion.png", height = 8, width = 8)

For each source the average distribution of each topic is calculated.

topicmean_news <- theta %>%
  filter(type == "news") %>%
  group_by(topic,joint_label,source) %>%
  summarise(frequency = mean(theta, na.rm = T)) %>% 
  ungroup()

topicmean_press <- theta %>%
  filter(type == "press") %>%
  group_by(topic,joint_label, source) %>%
  summarise(frequency = mean(theta, na.rm = T)) %>% 
  ungroup()

News articles

topicmean_news %>%
  ggplot(aes(reorder(joint_label,desc(topic)),
             frequency, fill=frequency)) +
  geom_col(show.legend = F) +
  coord_flip() +
  theme_hc() +
  facet_grid(~source) +
  #scale_fill_gradient2(limits=c(0,0.25), low="white", mid="blue", high="red") +
  scale_y_continuous(limits = c(0,0.25), breaks = c(0,0.1,0.2)) +
  labs(x=NULL, y=NULL) 

  #theme(axis.text.y = element_blank())

#ggsave("../figs/topic_proportion_news.png", width = 10, height =8)

Press releases

topicmean_press %>%
  ggplot(aes(reorder(joint_label,desc(topic)),
             frequency, fill=frequency)) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
    theme_hc() +
  facet_grid(~source) +
  #scale_fill_gradient2(limits=c(0,0.25), low="white", mid="blue", high="red") +
  scale_y_continuous(limits = c(0,0.25), breaks = c(0,0.1,0.2)) +
  labs(x=NULL, y=NULL) 

#ggsave("../figs/topic_proportion_press.png", width = 10, height =8)

Weighted topic probability

To combine the sentiment value with the topic probability of each document, the sentiment score is multiplied with the \(k\)x\(1\) vector for each document. Subsequently, to calculate the average topic sentiment for a source \(s\), the mean value of all documents belonging to source \(s\) is calculated. This results in a \(k\)x\(1\) vector representing the mean distributions \(\bar{ \theta_{s} }\) of this source, weighted by the sentiment scores:

\[ \bar{ \theta_{s} } = \begin{bmatrix} \bar{ \theta_{1} } \\ . \\ . \\ \bar{ \theta_{k} } \\ \end{bmatrix} \]

theta <- theta %>% mutate(sentiment_theta = sentiment*theta)

topicsent_news <- theta %>%
  filter(type == "news") %>%
  group_by(topic,joint_label,source) %>%
  dplyr::summarise(sent_theta = mean(sentiment_theta, na.rm = T)) %>% 
  ungroup()

topicsent_press <- theta %>%
  filter(type == "press") %>%
  group_by(topic,joint_label, source) %>%
  summarise(sent_theta = mean(sentiment_theta, na.rm = T)) %>% 
  ungroup()
topicsent_news %>%
  mutate(sent_theta = sent_theta*1000) %>%
  ggplot(aes(reorder(joint_label,desc(topic)),
             sent_theta, fill=sent_theta)) +
  geom_col(show.legend = F) +
  coord_flip() +
  theme_hc() +
  facet_grid(.~source) +
    scale_y_continuous(limits = c(-0.7,0.2), breaks = c(-0.6,-0.4,-0.2,0)) +
  #scale_fill_gradient2(limits=c(0,0.25), low="white", mid="blue", high="red") +
  labs(x=NULL, y=NULL) +
  theme(axis.text.x = element_text(size = 6)
        #axis.text.y = element_blank()
        )

#ggsave("../figs/topic_sent_news.png", width = 10, height =8)
topicsent_press %>%
  mutate(sent_theta = sent_theta*1000) %>%
  ggplot(aes(reorder(joint_label,desc(topic)),
             sent_theta, fill=sent_theta)) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
    theme_hc() +
  facet_grid(~source) +
  #scale_fill_gradient2(limits=c(0,0.25), low="white", mid="blue", high="red") +
  scale_y_continuous(limits = c(-0.7,0.2), breaks = c(-0.6,-0.4,-0.2,0)) +
  labs(x=NULL, y=NULL) +
  theme(axis.text.x = element_text(size = 6))

#ggsave("../figs/topic_sent_press.png", width = 10, height =8)

Correlation of weighted topic distribution

On the basis of thes weighted topic distributions, the bivariate Pearson correlation coefficients are calculated for each pair of media outlet and party. The higher the correlation coefficient, the higher the slant index for a party in a media outlet.

library(Hmisc)
library(ggcorrplot)
library(corrr)

news <- c("DIE WELT","stern.de", "ZEIT ONLINE", "FOCUS Online", "Bild.de", "SPIEGEL ONLINE", "tagesschau.de" )
parties <- c("CDU", "SPD", "AfD", "B90/GRÜNE", "DIE LINKE", "FDP" )

corr.df <- bind_rows(topicsent_press, topicsent_news) %>% 
  select(-joint_label) %>% spread(source, sent_theta)

x <- as.matrix(corr.df[,-1])

rs <- correlate(x)
rs %>% 
  network_plot()

library(patchwork)

plot <- rs %>%
  as_tibble() %>%
  dplyr::rename(source1 = rowname) %>%
  gather(key = source2, value = corr, -source1) %>% 
  mutate(
    type1 = ifelse(source1 %in% news, "news", "party"),
    type2 = ifelse(source2 %in% news, "news", "party")
  ) 

p1 <- plot %>%
  filter(type1 == "news") %>%
  filter(type2 == "news") %>%
  ggplot(aes(source1, source2, 
                 label=round(corr, digits = 2),
                 fill=corr)) +
  geom_tile() +
  scale_fill_gradient2(low = color, high = color1) +
  geom_text(size=1.8) +
  theme_hc() +
  labs(x = NULL, y = NULL, title = "Medium / Medium") + 
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +
  theme(legend.position = "none",
        axis.text = element_text(size = 6),
       axis.text.x = element_text(angle = 90))

p2 <- plot %>%
  filter(type1 == "news") %>%
  filter(type2 == "party") %>%
  ggplot(aes(source1, source2, 
                 label=round(corr, digits = 2),
                 fill=corr)) +
  geom_tile() +
  scale_fill_gradient2(low = color, high = color1) +
  geom_text(size=1.8) +
  theme_hc() +
  labs(x = NULL, y = NULL, title = "Medium / Partei") + 
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +
  theme(legend.position = "none",
        axis.text = element_text(size = 6),
        #axis.text.y = element_blank(),
       axis.text.x = element_text(angle = 90))

p3 <- plot %>%
  filter(type1 == "party") %>%
  filter(type2 == "party") %>%
  ggplot(aes(source1, 
             source2,label=round(corr, digits = 2), fill=corr)) +
  geom_tile() +
  scale_fill_gradient2(low = color, high = color1) +
  geom_text(size=1.8) +
  theme_hc() +
  labs(x = NULL, y = NULL, title = "Partei / Partei") + 
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +
  theme(legend.position = "none",
        axis.text = element_text(size = 6),
       axis.text.x = element_text(angle = 90))

p1 + p2 + p3

#ggsave("../figs/corrplot.png", width = 10, height =4)

Radar chart

radar <- plot %>%
  filter(type1 == "party") %>%
  filter(type2=="news") %>%
  select(source1, source2, corr) %>%
  spread(key = source1, value = corr)
ggiraphExtra::ggRadar(radar, aes(color = source2),
                      rescale = F,
                      interactive = T,
                      alpha = 0) 
# Print out the dataframe to a latex table 
xt <- xtable(radar,
             digits = c(0,3,3,3,3,3,3,3),
             type="latex",
             caption ="Topic correlation")

print(xt, include.rownames = F, file="../writing/tables/correlation.tex" )

Rescale the values

radar_rescaled <- as_tibble(sapply(radar[,-1], function(x) normalize_data2(x) ) ) %>%
  mutate(medium = radar$source2)
ggiraphExtra::ggRadar(radar_rescaled, 
                      aes(color = medium),
                      rescale = F,
                      interactive = T,
                      alpha = 0)

Compare with reader preferences

Since 2012, the Reuters Institute Digital News Survey has been investigating the media use of digital content. Among others, the following questions are investigated: What websites do you visit to access news online? What is your political orientation?

The study is being conducted simultaneously in 37 countries under the coordination of the Reuters Institute for the Study of Journalism, based in Oxford (UK). The Hans Bredow Institute has been responsible for the German part of the study as a cooperation partner since 2013. Fieldwork was undertaken between 19th - 22nd January 2018 conducting an online survey. Total sample size for Germany was 2038 adults (aged 18+) who access news once a month or more.

reutersDF1 <- readxl::read_excel("../data/reuters_clean.xlsx")
reutersDF2 <- readxl::read_excel("../data/reuters_clean.xlsx", sheet = "orientation")

Q5b. Which of the following brands have you used to access news online in the last week (via websites, apps, social media, and other forms of Internet access)? Please select all that apply.

Which of the brands have you used to access news online in the last week?

Q1F. Some people talk about ‘left’, ‘right’ and ‘centre’ to describe parties and politicians. (Generally socialist parties would be considered ‘left wing’ whilst conservative parties would be considered ‘right wing’). With this in mind, where would you place yourself on the following scale?

reutersDF.long <- reutersDF1 %>%
  select(medium:`Very rightwing`) %>%
  mutate(order = Centre...5) %>%
  gather(partisan, count, `Very leftwing`:`Very rightwing`) %>%
  mutate(partisan = ifelse(partisan == "Centre...5", "Centre", partisan)) %>%
  mutate(partisan.f = as.factor(partisan)) %>%
  mutate(
    partisan_scale = ifelse(partisan == "Very leftwing", -3,NA),
         partisan_scale = ifelse(partisan == "Fairly leftwing", -2, partisan_scale),
         partisan_scale = ifelse(partisan == "Slightly leftofcentre", -1,partisan_scale),
         partisan_scale = ifelse(partisan == "Centre", 0, partisan_scale),
         partisan_scale = ifelse(partisan == "Slightly rightofcentre", 1, partisan_scale),
         partisan_scale = ifelse(partisan == "Fairly rightwing",2, partisan_scale),
         partisan_scale = ifelse(partisan == "Very rightwing", 3, partisan_scale)
  ) 

reutersDF.long <- reutersDF.long %>%
  
  # group by partisan 
  group_by(partisan.f) %>%
  mutate(count_sum_p = sum(count)) %>%
  ungroup() %>%
  
  # group by medium
  group_by(medium) %>%
  mutate(count_sum_m = sum(count)) %>%
  ungroup() %>%
  
  # calulate relative counts by partisan and medium
  mutate(count_relative_p = count/count_sum_p,
         count_relative_m = count/count_sum_m,
         order_relative_p = order/count_sum_p,
         order_relative_m = order/count_sum_m,
         insample = ifelse(medium %in% keeps, "Yes", "No")
         )
p1 <- reutersDF.long %>%
  filter(!grepl("know",medium)) %>%
  filter(!grepl("None",medium)) %>%
  filter(medium %in% keeps) %>%
  mutate(
    label = (count_relative_m*100),
    label_color = ifelse(label > 18, "white", "black")
  ) %>%
  ggplot(aes(reorder(partisan.f, partisan_scale),
             reorder(medium, order_relative_m), 
             fill = count_relative_m)) +
  geom_tile() +
  scale_fill_gradient2(low = color, high = color1) +
  geom_text(aes(label=round(label, digits = 1)), color = color3, size = 2) +
  #scale_color_manual(values = c("black"=color3, "white"="white")) +
  theme_hc() +
  labs(x = NULL, y = NULL) + 
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +
  theme(legend.position = "none",
        axis.text = element_text(size = 6),
       axis.text.x = element_text(angle = 90))

p2 <- reutersDF.long %>%
  filter(!grepl("know",medium)) %>%
  filter(medium %in% keeps) %>%
  group_by(medium) %>%
  summarise(count_sum = sum(count),
            order_relative_m = sum(order_relative_m)) %>%
  ggplot(aes(reorder(medium,order_relative_m), count_sum)) +
  geom_col(fill = color1) +
  geom_text(aes(label=count_sum), hjust = 1, size = 2, color = "white") +
  coord_flip() +
  theme_hc() +
  labs(x = NULL, y = NULL) +
  theme(axis.text.y = element_blank(),
        axis.text = element_text(size = 6),
        axis.ticks.y = element_blank()
        )

p1 + p2 + plot_layout(widths = c(2,1))

#ggsave("../figs/reuters3.png", width = 5, height = 3)

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